| Subject ID | pg36 |
| Test date and time | 2018-01-26 10:34 |
| Test battery | template_05_subset_00, template_02_subset_00, Template_elel, template_06_subset_00, template_06_subset_04, template_04_subset_00, template_04_subset_00_offset, template_00_subset_00 (format) |
Parkinson’s Disease Evaluation
This report is intended to assist qualified Health Care Profesionals (HCP) in the assessment of an individual referred under the suspicion of having Parkinson’s Disease.
Clinical Context
This report presents several AI metrics derived from objective measures from individuals performing a battery of test using Manus Neurodynamica NeuroMotor PenTM.
These presented metrics combine factors from detailed measurement recordings made whilst the individual performs a battery of well established neurological test tasks.
The metrics have been assessed in a UK reference population and an individual’s results are presented in this clinical context. The HCP should review Clinical,Reference, Study et al [1] to establish applicability and limitations.
The information in this report should be used in the context of a full neurological assessment conducted to the current standard of care practices to establish a diagnosis.
Subject and Recording Details
Overall Assessment
Subject performance similar to PD population (recommend review of report details)
In the clinical reference population, 41 individuals with a value less than 0.85 were subsequently diagnosed with PD. That is, 97.62% of the PD diagnoses in the study.
Additionally, 0 individuals with a value greater or equal to 0.85 were subsequently diagnosed as non PD. That is, 0.0% of the non PD diagnoses in the study.
Symptom Scores
These mini boxplots show the scores in a clinical context. Currently against the ‘Walker study’ data. A bigger pool would be much better (so max 83 individuals, usually lower if raw data did not result in successful classification).
Micrographia
The micrographia symptom assessment is derived from a combination of factors in the elel task.
{'FN': 19, 'TN': 13, 'TP': 23, 'FP': 17}
Tremor
The tremor score is a combination of features in the circle, spiral and both zizag tasks.
{'FN': 4, 'TN': 5, 'TP': 38, 'FP': 25}
Bradykinesia
The bradykinesia score is a combination of features in the circle, spiral, both zizag and elel tasks.
{'FN': 9, 'TN': 8, 'TP': 33, 'FP': 22}
Spatial Accuracy
The accuracy score is a combination of factors in the spiral, both zigzags and both Fitts tasks.
{'FN': 42, 'TN': 30, 'TP': 0, 'FP': 0}
Test Battery Details
2023-03-10 07:57:44.481 | INFO | neuromotor_pen.data:_parse_header:698 - Loading Old Data Version...
ManusData_pg36.JSON
we need the signals file
Circle
/usr/local/lib/python3.9/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide
c /= stddev[:, None]
/usr/local/lib/python3.9/site-packages/numpy/lib/function_base.py:2855: RuntimeWarning: invalid value encountered in divide
c /= stddev[None, :]
Circle Segment 1
Duration 9.48 s, Accuracy Estimate 3.898 (lower is better)
Circle Segment 2
Duration 8.56 s, Accuracy Estimate 4.122 (lower is better)
Circle Segment 3
Duration 7.92 s, Accuracy Estimate 4.383 (lower is better)
Circle Segment 4
Duration 7.44 s, Accuracy Estimate 5.246 (lower is better)
Circle Segment 5
Duration 7.16 s, Accuracy Estimate 5.585 (lower is better)
Spiral
Spiral Segment 1
Duration 21.0 s, Accuracy Estimate 6.557 (lower is better)
Spiral Segment 2
Duration 29.32 s, Accuracy Estimate 2.716 (lower is better)
Spiral Segment 3
Duration 32.28 s, Accuracy Estimate 2.649 (lower is better)
Spiral Segment 4
Duration 28.36 s, Accuracy Estimate 2.814 (lower is better)
Spiral Segment 5
Duration 25.2 s, Accuracy Estimate 2.786 (lower is better)
Spiral Segment 6
Duration 30.72 s, Accuracy Estimate 3.045 (lower is better)
Spiral Segment 7
Duration 28.04 s, Accuracy Estimate 2.967 (lower is better)
Spiral Segment 8
Duration 25.2 s, Accuracy Estimate 2.617 (lower is better)
Spiral Segment 9
Duration 24.04 s, Accuracy Estimate 2.695 (lower is better)
Elel
Elel Segment 1
Elel Segment 2
Elel Segment 3
Elel Segment 4
Elel Segment 5
Elel Segment 6
Elel Segment 7
Elel Segment 8
Elel Segment 9
Elel Segment 10
Elel Segment 11
FITTS Short Modified
Can’t do fitts_short because low (or drifting) correlation between pressure and force
FITTS Long Modified
Can’t do fitts_long because low (or drifting) correlation between pressure and force
ZigZag
ZigZag Segment 1
Duration 15.08 s, Accuracy Estimate 5.399 (lower is better)
ZigZag Segment 2
Duration 14.52 s, Accuracy Estimate 4.401 (lower is better)
ZigZag Segment 3
Duration 15.24 s, Accuracy Estimate 3.938 (lower is better)
ZigZag Segment 4
Duration 17.72 s, Accuracy Estimate 4.511 (lower is better)
ZigZag Segment 5
Duration 18.72 s, Accuracy Estimate 2.957 (lower is better)
ZigZag Offset
ZigZag Offset Segment 1
Duration 13.68 s, Accuracy Estimate 0.038 (lower is better)
ZigZag Offset Segment 2
Duration 13.56 s, Accuracy Estimate 0.059 (lower is better)
ZigZag Offset Segment 3
Duration 10.96 s, Accuracy Estimate 0.07 (lower is better)
ZigZag Offset Segment 4
Duration 10.76 s, Accuracy Estimate 0.047 (lower is better)
ZigZag Offset Segment 5
Duration 11.56 s, Accuracy Estimate 0.04 (lower is better)
sentence
Can’t do sentence because Missing tablet or pen data
/usr/local/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.
return _methods._mean(a, axis=axis, dtype=dtype,
/usr/local/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide
ret = ret.dtype.type(ret / rcount)
Appendices
Misc
Currently a dumping ground for things that could be included or previous output style.
Putting all the results out here but will not be in a final report.
| HiSpec | {‘HiSpec_class’: ‘NOT PD’, ‘HiSpec_score’: 0.67} |
| RanFor | {‘RanFor_class’: ‘PD’, ‘RanFor_score’: 0.85} |
| BM_May22 | {‘BM_May22_class’: ‘NOT PD’, ‘BM_May22_score’: 0.5302306733632567} |
| BM_HC_Sep22 | {‘BM_HC_Sep22_class’: ‘Patient’, ‘BM_HC_Sep22_score’: 0.934793936423459} |
| BM_PD_Sep22 | {‘BM_PD_Sep22_class’: ‘PD’, ‘BM_PD_Sep22_score’: 0.7901971247215468} |